The Traveling Salesman Problem
A Computational Study
1st Edition
Published on 19. September 2011
608 pages
978-1-4008-4110-3 (ISBN)
Description
This book presents the latest findings on one of the most intensely investigated subjects in computational mathematics--the traveling salesman problem. It sounds simple enough: given a set of cities and the cost of travel between each pair of them, the problem challenges you to find the cheapest route by which to visit all the cities and return home to where you began. Though seemingly modest, this exercise has inspired studies by mathematicians, chemists, and physicists. Teachers use it in the classroom. It has practical applications in genetics, telecommunications, and neuroscience.
The authors of this book are the same pioneers who for nearly two decades have led the investigation into the traveling salesman problem. They have derived solutions to almost eighty-six thousand cities, yet a general solution to the problem has yet to be discovered. Here they describe the method and computer code they used to solve a broad range of large-scale problems, and along the way they demonstrate the interplay of applied mathematics with increasingly powerful computing platforms. They also give the fascinating history of the problem--how it developed, and why it continues to intrigue us.
The authors of this book are the same pioneers who for nearly two decades have led the investigation into the traveling salesman problem. They have derived solutions to almost eighty-six thousand cities, yet a general solution to the problem has yet to be discovered. Here they describe the method and computer code they used to solve a broad range of large-scale problems, and along the way they demonstrate the interplay of applied mathematics with increasingly powerful computing platforms. They also give the fascinating history of the problem--how it developed, and why it continues to intrigue us.
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More details
Edition
Course Book
Language
English
Place of publication
Princeton, NJ
United States
Publishing group
De Gruyter
Target group
Professional and scholarly
Edition type
Digital original
Product notice
Reflowable
Illustrations
200 line illus.
File size
15,76 MB
ISBN-13
978-1-4008-4110-3 (9781400841103)
Schweitzer Classification
Other editions
Additional editions
David L. Applegate | Robert E. Bixby | Vasek Chvatal
The Traveling Salesman Problem
A Computational Study
Book
02/2007
Princeton University Press
€87.30
Shipment within 10-20 days
Persons
David L. Applegate is a researcher at AT&T Labs. Robert E. Bixby is Research Professor of Management and Noah Harding Professor of Computational and Applied Mathematics at Rice University. Vasek Chvátal is Canada Research Chair in Combinatorial Optimization at Concordia University. William J. Cook is Chandler Family Chair in Industrial and Systems Engineering at the Georgia Institute of Technology.
Content
Preface xi
Chapter 1: The Problem 1
1.1 Traveling Salesman 1
1.2 Other Travelers 5
1.3 Geometry 15
1.4 Human Solution of the TSP 31
1.5 Engine of Discovery 40
1.6 Is the TSP Hard? 44
1.7 Milestones in TSP Computation 50
1.8 Outline of the Book 56
Chapter 2: Applications 59
2.1 Logistics 59
2.2 Genome Sequencing 63
2.3 Scan Chains 67
2.4 Drilling Problems 69
2.5 Aiming Telescopes and X-Rays 75
2.6 Data Clustering 77
2.7 Various Applications 78
Chapter 3: Dantzig, Fulkerson, and Johnson 81
3.1 The 49-City Problem 81
3.2 The Cutting-Plane Method 89
3.3 Primal Approach 91
Chapter 4: History of TSP Computation 93
4.1 Branch-and-Bound Method 94
4.2 Dynamic Programming 101
4.3 Gomory Cuts 102
4.4 The Lin-Kernighan Heuristic 103
4.5 TSP Cuts 106
4.6 Branch-and-Cut Method 117
4.7 Notes 125
Chapter 5: LP Bounds and Cutting Planes 129
5.1 Graphs and Vectors 129
5.2 Linear Programming 131
5.3 Outline of the Cutting-Plane Method 137
5.4 Valid LP Bounds 139
5.5 Facet-Inducing Inequalities 142
5.6 The Template Paradigm for Finding Cuts 145
5.7 Branch-and-Cut Method 148
5.8 Hypergraph Inequalities 151
5.9 Safe Shrinking 153
5.10 Alternative Calls to Separation Routines 156
Chapter 6: Subtour Cuts and PQ-Trees 159
6.1 Parametric Connectivity 159
6.2 Shrinking Heuristic 164
6.3 Subtour Cuts from Tour Intervals 164
6.4 Padberg-Rinaldi Exact Separation Procedure 170
6.5 Storing Tight Sets in PQ-trees 173
Chapter 7: Cuts from Blossoms and Blocks 185
7.1 Fast Blossoms 185
7.2 Blocks of G1/2 187
7.3 Exact Separation of Blossoms 191
7.4 Shrinking 194
Chapter 8: Combs from Consecutive Ones 199
8.1 Implementation of Phase 2 202
8.2 Proof of the Consecutive Ones Theorem 210
Chapter 9: Combs from Dominoes 221
9.1 Pulling Teeth from PQ-trees 223
9.2 Nonrepresentable Solutions also Yield Cuts 229
9.3 Domino-Parity Inequalities 231
Chapter 10: Cut Metamorphoses 241
10.1 Tighten 243
10.2 Teething 248
10.3 Naddef-Thienel Separation Algorithms 256
10.4 Gluing 261
Chapter 1: The Problem 1
1.1 Traveling Salesman 1
1.2 Other Travelers 5
1.3 Geometry 15
1.4 Human Solution of the TSP 31
1.5 Engine of Discovery 40
1.6 Is the TSP Hard? 44
1.7 Milestones in TSP Computation 50
1.8 Outline of the Book 56
Chapter 2: Applications 59
2.1 Logistics 59
2.2 Genome Sequencing 63
2.3 Scan Chains 67
2.4 Drilling Problems 69
2.5 Aiming Telescopes and X-Rays 75
2.6 Data Clustering 77
2.7 Various Applications 78
Chapter 3: Dantzig, Fulkerson, and Johnson 81
3.1 The 49-City Problem 81
3.2 The Cutting-Plane Method 89
3.3 Primal Approach 91
Chapter 4: History of TSP Computation 93
4.1 Branch-and-Bound Method 94
4.2 Dynamic Programming 101
4.3 Gomory Cuts 102
4.4 The Lin-Kernighan Heuristic 103
4.5 TSP Cuts 106
4.6 Branch-and-Cut Method 117
4.7 Notes 125
Chapter 5: LP Bounds and Cutting Planes 129
5.1 Graphs and Vectors 129
5.2 Linear Programming 131
5.3 Outline of the Cutting-Plane Method 137
5.4 Valid LP Bounds 139
5.5 Facet-Inducing Inequalities 142
5.6 The Template Paradigm for Finding Cuts 145
5.7 Branch-and-Cut Method 148
5.8 Hypergraph Inequalities 151
5.9 Safe Shrinking 153
5.10 Alternative Calls to Separation Routines 156
Chapter 6: Subtour Cuts and PQ-Trees 159
6.1 Parametric Connectivity 159
6.2 Shrinking Heuristic 164
6.3 Subtour Cuts from Tour Intervals 164
6.4 Padberg-Rinaldi Exact Separation Procedure 170
6.5 Storing Tight Sets in PQ-trees 173
Chapter 7: Cuts from Blossoms and Blocks 185
7.1 Fast Blossoms 185
7.2 Blocks of G1/2 187
7.3 Exact Separation of Blossoms 191
7.4 Shrinking 194
Chapter 8: Combs from Consecutive Ones 199
8.1 Implementation of Phase 2 202
8.2 Proof of the Consecutive Ones Theorem 210
Chapter 9: Combs from Dominoes 221
9.1 Pulling Teeth from PQ-trees 223
9.2 Nonrepresentable Solutions also Yield Cuts 229
9.3 Domino-Parity Inequalities 231
Chapter 10: Cut Metamorphoses 241
10.1 Tighten 243
10.2 Teething 248
10.3 Naddef-Thienel Separation Algorithms 256
10.4 Gluing 261
